class_docstr: This task configuration loads a specified model from the Hugging Face hub and runs it as a private endpoint while
  forwarding the port to the local machine.
  
  Args:
      model_name: The model that you want to deploy from the Hugging Face hub. E.g. gpt2, gpt2-xl, bert, etc.
      env: The list of environment variables, e.g. `"HUGGING_FACE_HUB_TOKEN"`, `"WANDB_API_KEY"`, `"WANDB_PROJECT"`, etc.
      quantize: Whether you want the model to be quantized. Supported values: `"awq"`, `"eetq"`, `"gptq"`, and `"bitsandbytes"`.
      dtype: The dtype to be forced upon the model. This option cannot be used with `quantize`.
      local_port: The local port to forward the traffic to.
code: def __init__(
      self,
      model_name: str,
      env: Dict[str, str] = None,
      quantize: Optional[str] = None,
      dtype: Optional[str] = None,
      local_port: int = 80,
  ):
      commands, image, port, env = get_configuration(
          model_name, env, quantize, dtype
      )
      super().__init__(
          commands=commands,
          image=image,
          ports=[f"{local_port}:{port}"],
          env=env,
      )
  
file_path: dstack/src/dstack/api/_public/huggingface/completions/__init__.py
imports: import os
  from typing import Dict, List, Optional, Tuple
  from dstack._internal.core.models.configurations import ServiceConfiguration, TaskConfiguration
label_desc: Use image node_id 1 to create a new CompletionTask object from inherited base classes: TaskConfiguration with example: obj = CompletionTask(model_name, env, quantize, dtype, local_port)